With the popularity of Deep Neural Networks (DNNs) in past few years, the feature engineering is mainly performed by DNNs and researchers focus more on designing the DNNs. Nevertheless, for some problems like COPs, processing the raw representation of problem instances in order to derive effective state representation improves the quality of the solution. For this reason, many research focus on developing methods for transforming the original observation of the environment to state representation. In [1]}, an approach named structure2vec for representing structured data like trees and graphs is introduced. This approach is based on the idea of embedding latent variable models into feature space. A vector for representing graph is obtained by employing probabilistic kernels to find latent variable models and a neural network is trained to output the embedding of a graph based on nodes' attributes. This idea is used in [2]} for solving graph-based combinatorial optimization problems such as Minimum Vertex Cover and TSP. The graph embedding network is learned by fitted Q-learning and the output of the network is used as a greedy policy to incrementally create the solution of the problem.
